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Table 1 Hurdles in precision medicine and precision public health within data, study, model development, and deployment phases

From: Big data hurdles in precision medicine and precision public health

Precision medicine
 • Concentration on individualized treatment and neglect of time component of predictions, i.e. early risk vs. differential diagnosis vs. post-treatment survival
 • Too much focus on genetics and –omics
 • Research on actionable factors vs. immutable risk factors
 • Integration of multi-omics
 • Integration of multi-domain data (e.g. genetics, diet, lifestyle, social)
Precision public health
 • Definition of target units (e.g. ethnic groups, geographic zones, social groups)
 • Conflict with precision medicine, i.e. individual-centric objectives (benefit of the single may not translate into benefit of the population)
 • Population-level outcomes
Data sources Study designs Prediction modelling Translational relevance
 • Heterogeneous data sources
 • Unstructured data sources
 • Lack of data on social determinants of health
 • Measurement issues (e.g. incompleteness, inaccuracy, imprecision in self-reported data)
 • Privacy and security
 • Cost
 • Limited adoption of common data models
• Semantic data integration (i.e. linking data elements by their meaning)
• Large longitudinal cohorts
• Ontology integration
• Ontology appropriateness (e.g. ontologies made for billing vs. for diagnostic purposes)
• Semantic interoperability
• Automated study design
• Biases of all sorts (e.g. protopathic)
• Confounding
• Causal inference
• Black-boxes vs. white-boxes (i.e. interpretability vs. performance)
• Complexity-based model selection
• Benchmark development
• Pragmatic interoperability (reproducibility, replicability, generalizability)
• Limited individual empowerment
• Disconnect from relevant clinical research
• Personal health record/health avatar (besides provider’s electronic records)
• Acceptance of artificial intelligence as integral part of doctors’ tools
• Learning systems
• Ethical usage and dissemination of modelling algorithms
• Redefining disease phenotype